来源: dennis_sy
Moore's Law已经被芯片制造工业推到了极限，单个半导体transistor只有几个纳米（TSMC最小的已经可以做到3nm，R&D应该能做更小，但量产不易）。如果你不理解这个意义，这么说吧，几个纳米的硅片，是由不到100个硅原子组成的。这么小的尺寸，使得芯片的设计者，不得不开始考虑以前可以忽略的物理原理（比如量子效应）。
为了生产这么小尺寸的芯片，需要买ASML最新的13.5nm波长的EUV光刻机，及相应的配套工艺设备（比如刻蚀，镀膜，检测和修复等）。这些半导体制造设备，造价高昂（比如，一台ASML EUV定价超过$150million)，除了业内几个巨头（TSMC，Samsung，Intel，GlobalFoundry等），已经没有什么公司可以负担得起。这导致两个非常明显的趋势： 1. 大部分芯片公司，本身没有制造能力，必须把自己设计的芯片交给像TSMC这样的公司代工；2. 高性能的芯片研发进展缓慢，代价高昂。
当Moore's Law开始显出疲态的时候，另一个领域的进展却如火如荼，那就是由AI算法引领的智能、低能耗芯片，比如智能手机（apple，andoid），可穿戴电子设备（apple watch），智能家居产品（google nest, amazon alexa)，自动驾驶控制芯片（Intel's mobileye）等等。这个其实很好理解，相比人类社会发展的其它方面，芯片工业的进步其实有点超前太多。现在迫切需要的是把芯片工业的领先技术，推广到人类社会生活的更多的应用场景中去。而各种小型，低能耗的智能产品，正是推动这一潮流的最佳结合点。
这个潮流，有点类似于历史上IBM的大型mainframe电脑被小型的桌面电脑取代的过程。那种动不动价格超过百万的mainframe computer, 类似于现在的cloud，功能强大，运算能力惊人，唯一的缺点就是不适合大范围推广应用。集成的AI芯片，能让部分cloud功能在小型的电子设备上得到实现；某些特定的cloud功能，还可通过在cloud上运算后，通过互联网传回到本地的小型终端。虽然目前这种智能芯片功能相对单一，但假以时日，其发展前景不可限量。
WSJ article: Huang's law is the new Moore's Law, and explains why Nvdia wants ARM
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”Over the last three to five years, machine-learning networks have been increasing by orders of magnitude in efficiency, says Dennis Laudick, vice president of marketing in Arm's machine-learning group. “Now it's more about making things work in a smaller and smaller environment,” he adds. Arm's smallest and most energy-sipping chips, tiny enough to be powered by a watch battery, can now enable cameras to recognize objects in real time.
This movement of AI processing from the cloud to the “edge”—that is, on the devices themselves—explains Nvidia's desire to buy Arm, says Nexar co-founder and CEO Eran Shir. Nvidia has a near monopoly on AI processing in the cloud. But where two years ago, Nexar performed 40% of its data processing in the cloud, Arm-based chips have enabled it to do much more of that processing in mobile devices, and faster, since it doesn't have to be transmitted over the internet first. Today, the cloud is doing only 15% of the work. In addition, some functions, like a vision-based parking assistant, were not even possible until recently, when the chips in phones became much more capable.“
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